Data science/ML/AI
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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist
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الشبكة:📈 نظرة تحليلية على قناة تيليجرام Data science/ML/AI
تُعد قناة Data science/ML/AI (@datascience_bds) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 14 053 مشتركاً، محتلاً المرتبة 9 084 في فئة التكنولوجيات والتطبيقات والمرتبة 28 047 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 14 053 مشتركاً.
بحسب آخر البيانات بتاريخ 31 أكتوبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 57، وفي آخر 24 ساعة بمقدار 1، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 9.70%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 2.13% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 1 363 مشاهدة. وخلال اليوم الأول يجمع عادةً 299 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 4.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل panda, learning, row, api, ethic.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Data science and machine learning hub
Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources.
For beginners, data scientists and ML engineers
👉 https://rebrand.ly/bigdatachannels
DMCA: @disclosure_bds
Contact: @mldatasci...”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 01 نوفمبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.
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Free Data Courses
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Join @datascience_bds for more cool data science materials.
*This channel belongs to* @bigdataspecialist group
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![Data Science and Machine Learning [PDF] Mathematical and Statistical Methods Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre,](https://cdn.tlmtr.io/_next/static/media/empty-channel.35ed232f.svg)
Data Science and Machine Learning [PDF]
Mathematical and Statistical Methods
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8th May 2022
533 pages
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Free Big Data Courses
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Introduction to Big Data - an overview of the 10 V's
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Applications of Deep Neural Networks
Washington University in St. Louis
https://sites.wustl.edu/jeffheaton/t81-558/
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
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Hey folks, this week's round of our programming quiz league is about data science. Here is the quiz link:
http://t.me/QuizBot?start=H4Ow9sU8
Feel free to answer on those 8 short questions and let me know about your placement on final score.
Also to those who celebrate today I wish Merry Christmas 🎄🥳😊
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#deep_learning #pyTorch #numPy #MXNet #TensorFlow #neural_networks
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
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Often the hardest part of solving a machine learning problem can be finding the right estimator for the job.
Different estimators are better suited for different types of data and different problems.
The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data.
Source: Scikit-learn
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Graphic SQL reference for data manipulation
Source: https://raw.githubusercontent.com/kelsfarmer/SQL/master/SQL%20Data%20Manipulation%20Language%20Cheat%20Sheet.png
